Sound like fine-tuning the pre-trained network for novel data is a suitable way to deal with the challenge.
But unfortunately, [1]} has demonstrated that this strategy with few labeled samples easily causes the overfitting problem. Hence, it usually provides limited improvements for FSC or even negatively affects the results.
Thus researchers usually give up fine-tuning and choose the decoupled paradigm, i.e., freezing the feature extractor after pre-training and directly extracting novel features.
